Standard Dual Quaternion Optimization and Its Applications in Hand-Eye Calibration and SLAM

Several common dual quaternion functions,such as the power function,the magnitude function,the 2-norm function,and the kth largest eigenvalue of a dual quaternion Hermitian matrix,are standard dual quaternion functions,i.e.,the standard parts of their function values depend upon only the standard parts of their dual quaternion variables.Furthermore,the sum,product,minimum,maximum,and composite functions of two standard dual functions,the logarithm and the exponential of standard unit dual quaternion functions,are still standard dual quaternion functions.On the other hand,the dual quaternion optimization problem,where objective and constraint function values are dual numbers but variables are dual quaternions,naturally arises from applications.We show that to solve an equality constrained dual quaternion optimization(EQDQO)problem,we only need to solve two quaternion optimization problems.If the involved dual quaternion functions are all standard,the optimization problem is called a standard dual quaternion optimization problem,and some better results hold.Then,we show that the dual quaternion optimization problems arising from the hand-eye calibration problem and the simultaneous localization and mapping(SLAM)problem are equality constrained standard dual quaternion optimization problems.

New self-calibration approach to space robots based on hand-eye vision

To overcome the influence of on-orbit extreme temperature environment on the tool pose(position and orientation) accuracy of a space robot,a new self-calibration method based on a measurement camera(hand-eye vision) attached to its end-effector was presented.Using the relative pose errors between the two adjacent calibration positions of the space robot,the cost function of the calibration was built,which was different from the conventional calibration method.The particle swarm optimization algorithm(PSO) was used to optimize the function to realize the geometrical parameter identification of the space robot.The above calibration method was carried out through self-calibration simulation of a six-DOF space robot whose end-effector was equipped with hand-eye vision.The results showed that after calibration there was a significant improvement of tool pose accuracy in a set of independent reference positions,which verified the feasibility of the method.At the same time,because it was unnecessary for this method to know the transformation matrix from the robot base to the calibration plate,it reduced the complexity of calibration model and shortened the error propagation chain,which benefited to improve the calibration accuracy.

Research on Hand-eye Calibration Technology of Visual Service Robot Grasping Based on ROS

For the hand-eye calibration of the vision service robot,the traditional hand-eye calibration tech-nology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose information of the service robot in real time.In this paper,a hand-eye calibration method based on ROS(Robot Operating System)is proposed.In this method,ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times.Meanwhile,the head camera of the service robot takes images of a fixed point in the scene.Then,the nonlinear equations were established ac-cording to the homography matrix of the two images and the position and pose information of the ROS system,and the accurate hand-eye relationship was optimized by the least square method.Finally,an experimental platform is built and the proposed hand-eye calibration method is verified.The experiment results show that the method is easy to operate,simple algorithm and correct result,which verifies the effectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.

IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s radomes.To improve the measurement accuracy of the OMM system,a novel Iteratively Automatic machine learning Boosted hand-eye Calibration(IABC)method is proposed.Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC.Firstly,a new objective function is derived,containing analytical parameters of the handeye relationship and LDS errors.Then,a hybrid calibration model composed of two kernels is proposed to solve the objective function.One kernel is the analytical kernel designed for solving analytical parameters.Another kernel is the automatic machine learning(AutoML)kernel designed to model LDS errors.The two kernels are connected with stepwise iterations to find the best calibration results.Compared with traditional methods,hand-eye experiments show that IABC reduces the calibration RMSE by about 50%.Verification experiments show that IABC reduces the measurement deviations by about 25%-50%and RMSEs within 40%.Even when the training data are obviously less than the test data,IABC performs well.Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.

Learning Robotic Hand-eye Coordination Through a Developmental Constraint Driven Approach

The skill of robotic hand-eye coordination not only helps robots to deal with real time environment,but also afects the fundamental framework of robotic cognition.A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination.However,several important features within infant developmental procedure have not been introduced into such approaches.This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants.The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination,and adopts a developmental mechanism from psychology to drive the robot.The entire learning procedure is driven by developmental constraint： The robot starts to act under fully constrained conditions,when the robot learning system becomes stable,a new constraint is assigned to the robot.After that,the robot needs to act with this new condition again.When all the contained conditions have been overcome,the robot is able to obtain hand-eye coordination ability.The work is supported by experimental evaluation,which shows that the new approach is able to drive the robot to learn autonomously,and make the robot also exhibit developmental progress similar to human infants.

A Hand-Eye Vision Measuring System for Articulate Robots

To make dynamic measurements for an articulate robot, a hand-eye vision measuring system is built up. This system uses two charge coupled device (CCD) cameras mounted on the end-effector of the robot. System analysis is based on the stereovision theory and line-matching technology, using a computer to evaluate the dynamic performance parameters of an articulate robot from the two images captured by the two cameras. The measuring procedure includes four stages, namely, calibration, sampling, image process-ing, and calculation. The path accuracy of an articulate industrial robot was measured by this system. The results show that this system is a low-cost, easy to operate, and simple system for the dynamic performance testing of articulate robots.

Development of a novel hand-eye calibration for intuitive control of minimally invasive surgical robot

Robotic-assisted surgical system has introduced a powerful platform through dexterous instrument and hand-eye coordination intuitive control.The knowledge of laparoscopic vision is a crucial piece of information for robot-assisted minimally invasive surgery focusing on improved surgical outcomes.Obtaining the transformation with respect to the laparoscope and robot slave arm frames using hand-eye calibration is essential,which is a key component for developing intuitive control algorithm.We proposed a novel two-step modified dual quaternion for hand-eye calibration in this study.The dual quaternion was exploited to solve the hand-eye calibration simultaneously and powered by an iteratively separate solution.The obtained hand-eye calibration result was applied to the intuitive control by using the hand-eye coordination criterion.Promising simulations and experimental studies were conducted to evaluate the proposed method on our surgical robot system.We extensively compared the proposed method with state-of-the-art methods.Results demonstrate this method can improve the calibration accuracy.The effectiveness of the intuitive control algorithm was quantitatively evaluated,and an improved hand-eye calibration method was developed.The relationship between laparoscope and robot kinematics can be established for intuitive control.

Development of a monocular vision system for robotic drilling

Robotic drilling for aerospace structures demands a high positioning accuracy of the robot, which is usually achieved through error measurement and compensation. In this paper, we report the development of a practical monocular vision system for measurement of the relative error between the drill tool center point(TCP) and the reference hole. First, the principle of relative error measurement with the vision system is explained, followed by a detailed discussion on the hardware components, software components, and system integration. The elliptical contour extraction algorithm is presented for accurate and robust reference hole detection. System calibration is of key importance to the measurement accuracy of a vision system. A new method is proposed for the simultaneous calibration of camera internal parameters and hand-eye relationship with a dedicated calibration board. Extensive measurement experiments have been performed on a robotic drilling system. Experimental results show that the measurement accuracy of the developed vision system is higher than 0.15 mm, which meets the requirement of robotic drilling for aircraft structures.

Haptic rendering for dental training system

Immersion and interaction are two key features of virtual reality systems, which are especially important for medical applications. Based on the requirement of motor skill training in dental surgery, haptic rendering method based on triangle model is investigated in this paper. Multi-rate haptic rendering architecture is proposed to solve the contradiction between fidelity and efficiency requirements. Realtime collision detection algorithm based on spatial partition and time coherence is utilized to enable fast contact determination. Proxy-based collision response algorithm is proposed to compute surface contact point. Cutting force model based on piecewise contact transition model is proposed for dental drilling simulation during tooth preparation. Velocity-driven levels of detail haptic rendering algorithm is proposed to maintain high update rate for complex scenes with a large number of triangles. Hapticvisual collocated dental training prototype is established using half-mirror solution. Typical dental operations have been realized including dental caries exploration, detection of boundary within dental cross-section plane, and dental drilling during tooth preparation. The haptic rendering method is a fundamental technology to improve immersion and interaction of virtual reality training systems, which is useful not only in dental training, but also in other surgical training systems.